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Subjective Probability Forecasts for Recessions


  • Kajal Lahiri

    () (Department of Economics, University of Albany, SUNY, Albany, NY, 12159, USA.)

  • J George Wang

    () (College of Staten Island, CUNY, Staten Island, NY, 10314, USA.)


Probabilistic forecasts are often more useful in business than point forecasts. In this paper, the joint subjective probabilities for negative GDP growth during the next two quarters obtained from the Survey of Professional Forecasters (SPF) are evaluated using various decompositions of the Quadratic Probability Score (QPS). Using the odds ratio and other forecasting accuracy scores appropriate for rare event forecasting, we find that the forecasts have statistically significant accuracy. However, compared to their discriminatory power, these forecasts have excess variability that is caused by relatively low assigned probabilities to forthcoming recessions. We suggest simple guidelines for the use of probability forecasts in practice.Business Economics (2006) 41, 26–37; doi:10.2145/20060204

Suggested Citation

  • Kajal Lahiri & J George Wang, 2006. "Subjective Probability Forecasts for Recessions," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 41(2), pages 26-37, April.
  • Handle: RePEc:pal:buseco:v:41:y:2006:i:2:p:26-37

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    References listed on IDEAS

    1. George L. Perry, 1973. "Capacity in Manufacturing," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 4(3), pages 701-742.
    2. Norman J. Morin & John J. Stevens, 2004. "Estimating capacity utilization from survey data," Finance and Economics Discussion Series 2004-49, Board of Governors of the Federal Reserve System (U.S.).
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    Cited by:

    1. Österholm, Pär, 2012. "The limited usefulness of macroeconomic Bayesian VARs when estimating the probability of a US recession," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 76-86.
    2. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    3. Song, ChiUng & Boulier, Bryan L. & Stekler, Herman O., 2009. "Measuring consensus in binary forecasts: NFL game predictions," International Journal of Forecasting, Elsevier, vol. 25(1), pages 182-191.
    4. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1200-7 is not listed on IDEAS
    5. Giordani, Paolo & Soderlind, Paul, 2003. "Inflation forecast uncertainty," European Economic Review, Elsevier, vol. 47(6), pages 1037-1059, December.
    6. Herman O. Stekler & Tianyu Ye, 2016. "Evaluating a Leading Indicator: An Application: the Term Spread," Working Papers 2016-004, The George Washington University, Department of Economics, Research Program on Forecasting.
    7. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
    8. Sergey V. Smirnov & Daria A. Avdeeva, 2016. "Wishful Bias in Predicting Us Recessions: Indirect Evidence," HSE Working papers WP BRP 135/EC/2016, National Research University Higher School of Economics.
    9. Galbraith, John W. & van Norden, Simon, 2011. "Kernel-based calibration diagnostics for recession and inflation probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1041-1057, October.

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